A Novel Image Processing-Based Method for Wind-Induced Vibration Response Prediction and Structural Monitoring of Long-Span Bridges

As a crucial component of modern transportation infrastructure, long-span bridges are subject to wind-induced vibrations that pose a key issue in structural safety studies.Windinduced vibrations not only lead to dynamic deformation of the bridge but may also cause structural fatigue and failure.Therefore, accurately predicting and monitoring the windinduced vibration responses is essential for ensuring the safe operation of bridges.With the rapid development of Digital Image Correlation (DIC) technology and machine learning algorithms, the integration of image processing and intelligent algorithms for monitoring and predicting wind-induced vibrations of long-span bridges has emerged as a promising research direction.Although significant progress has been made in monitoring and analyzing wind-induced vibrations, traditional methods often struggle with accurately capturing the evolution of minute deformations and achieving efficient predictions, thus limiting their practical application.This paper proposes a novel method for wind-induced vibration response prediction and structural monitoring of long-span bridges based on image processing.First, DIC technology is used to characterize the structural evolution of the bridge during wind-induced vibrations, capturing small deformations and local deformation features.Then, an image processing algorithm is applied to measure and calculate the structural evolution during the vibration response, enabling a quantitative analysis of the damage caused by wind load.Finally, by combining Whale Optimization Algorithm (WOA), Temporal Convolutional Network (TCN), and Self-Attention (SA) mechanism, a prediction model is developed to accurately forecast the bridge's dynamic response behavior under varying wind speeds.The study shows that this method effectively captures the spatiotemporal evolution characteristics of the wind-induced vibration process, enhancing the accuracy and reliability of the predictions, thus providing a scientific basis for the health monitoring and maintenance of long-span bridges.